{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import joblib\n",
    "import warnings\n",
    "import collections\n",
    "from itertools import zip_longest\n",
    "\n",
    "# from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "# from sklearn.gaussian_process.kernels import DotProduct, WhiteKernel, RBF, ConstantKernel\n",
    "\n",
    "# from sklearn.linear_model import LinearRegression\n",
    "\n",
    "from scipy.optimize import curve_fit\n",
    "import GPy\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(data_no=1): \n",
    "    data = joblib.load('./train_HA/train_HA_0%d.lz4'%data_no) \n",
    "    return data\n",
    "\n",
    "def moving_average(data, window_size, window_type='rectang'):\n",
    "    '''\n",
    "    描述：\n",
    "        window_type: triang or rectang\n",
    "    '''\n",
    "    if window_type == 'triang':\n",
    "        window = np.linspace(0, 2/(window_size+1), window_size+1)[1:]\n",
    "    elif window_type == 'rectang':\n",
    "        window = np.ones(int(window_size))/float(window_size)\n",
    "    else:\n",
    "        raise ValueError('window_type error!')\n",
    "        \n",
    "    return np.convolve(data, window, 'same')\n",
    "\n",
    "def df_moving_average(df, window_size, window_type='rectang', columns=None):\n",
    "    if columns is None:\n",
    "        columns = ['Current_1', 'Current_2' ,'Current_3']\n",
    "    \n",
    "    for col in columns:\n",
    "        df[col] = moving_average(df[col], window_size, window_type)\n",
    "    return df\n",
    "\n",
    "def impute_anomalies_rolling_std(y_old, window_size, sigma=.0, up_thred = 1000, down_thred = 0):\n",
    "    '''\n",
    "    描述：\n",
    "        使用triang窗取均值,检测并使用均值填充异常点\n",
    "    '''\n",
    "    y = y_old.copy()\n",
    "    y[y>20] = 10\n",
    "    avg = moving_average(y, window_size,window_type= 'triang')\n",
    "    avg_list = avg.tolist()\n",
    "    residual = y - moving_average(y, window_size, 'triang')\n",
    "    # Calculate the variation in the distribution of the residual\n",
    "    testing_std = pd.Series(residual).rolling(window=window_size, min_periods=1, center=False).std()\n",
    "    rolling_std = testing_std.replace(np.nan,testing_std.iloc[window_size-1]).values\n",
    "    \n",
    "    up_bound = avg + (sigma * rolling_std)\n",
    "    up_tmp = np.stack([up_bound, np.ones(up_bound.shape[0])*up_thred], axis=0)\n",
    "    up_bound = np.min(up_tmp, axis=0)\n",
    "    \n",
    "    down_bound = avg - (sigma * rolling_std)\n",
    "    down_tmp = np.stack([down_bound, np.ones(down_bound.shape[0])*down_thred], axis=0)\n",
    "    down_bound = np.max(down_tmp, axis=0)\n",
    "    \n",
    "    f = ((y>up_bound)|(y<down_bound)).values\n",
    "    y[f] = avg[f]\n",
    "    \n",
    "    return y\n",
    "\n",
    "\n",
    "\n",
    "# def window_fun(x, window_size):\n",
    "#     '''\n",
    "#     描述：\n",
    "#         使用triang窗函数，计算一段序列的最后一个点对应的均值\n",
    "#     '''\n",
    "#     window = np.linspace(0, 2/(window_size+1), window_size+1)[1:]\n",
    "#     delta = len(window) - len(x)\n",
    "#     if delta>0:\n",
    "#         tmp = np.array([0]*delta +　list(x))\n",
    "#         return np.sum(window*tmp)/np.sum(window[delta:])\n",
    "#     else:\n",
    "#         tmp = np.array(x[-len(window):])\n",
    "#         return np.sum(window*tmp)\n",
    "\n",
    "def predict_trend(x, window_size=8):\n",
    "    '''\n",
    "    描述：\n",
    "        使用二阶插值预测接下来的点\n",
    "    '''\n",
    "    ser = pd.Series(x)\n",
    "    x_train = ser.index[ser != -1].values[-window_size:]\n",
    "    y_train = ser[ser != -1].values[-window_size:]\n",
    "    \n",
    "    # 使用GPR预测趋势\n",
    "#     kernel = DotProduct() + WhiteKernel() # linear regression 效果类似\n",
    "#     kernel = RBF()\n",
    "#     reg = GaussianProcessRegressor(kernel=kernel, \n",
    "#                                random_state=0, \n",
    "#                                n_restarts_optimizer=3)\n",
    "    # 使用curve_fit 预测趋势\n",
    "#     def func(x, a, b):\n",
    "#         return a*np.exp(b/x)    \n",
    "    \n",
    "    if y_train.shape[0] <1:\n",
    "        return -1\n",
    "    elif y_train.shape[0] == 1:\n",
    "        return y_train[0]*1.1\n",
    "    else: \n",
    "       \n",
    "        # curve_fit 不需要reshape x_train\n",
    "        # reg.fit(x_train, y_train)\n",
    "        # reg.predict(np.array(ser.index.max()+1).reshape(-1,1))[0]\n",
    "#         popt, pcov = curve_fit(func, x_train, y_train)\n",
    "#         yvals = func(ser.index.max()+1,*popt) #拟合y值\n",
    "        f1 = np.polyfit(x_train, y_train, 2)\n",
    "        p1 = np.poly1d(f1)\n",
    "        yvals = p1(ser.index.max()+1)\n",
    "        return yvals\n",
    "    \n",
    "def CL_mean(df, apply_col, seg_col, up_thred = 30, down_thred= 0, use_pretict=True):\n",
    "    '''\n",
    "    描述：\n",
    "        统计每一段时间的某一列均值，并拟合曲线\n",
    "    '''\n",
    "    data = df[apply_col]\n",
    "    seg_idx = df[seg_col].unique()\n",
    "    seg_idx.sort()\n",
    "    \n",
    "    mean_list = []\n",
    "    for idx in seg_idx:\n",
    "        idx_mean = data[df[seg_col] == idx].mean()\n",
    "        cmp_mean = predict_trend(mean_list)\n",
    "        if cmp_mean == -1:\n",
    "            mean_list.append(idx_mean)    \n",
    "        elif use_pretict and (np.abs(idx_mean - cmp_mean) >= 1.5):\n",
    "            print(idx, idx_mean ,cmp_mean)\n",
    "            mean_list.append(cmp_mean)\n",
    "        else:\n",
    "            mean_list.append(idx_mean)\n",
    "         \n",
    "        # 满足阈值\n",
    "        if (mean_list[-1]>up_thred) or (mean_list[-1]<down_thred):\n",
    "            print('ha:',mean_list[-1], idx)\n",
    "            mean_list[-1] = -1\n",
    "            \n",
    "        if len(mean_list) == 1:\n",
    "            mean_list[0] = 0\n",
    "            \n",
    "#     for i in range(len(mean_list)):\n",
    "#         if i == 0:\n",
    "#             mean_list[i] = 0\n",
    "#         elif i == len(mean_list)-1:\n",
    "#             mean_list[i] = mean_list[i-1]*1.1\n",
    "#         else:\n",
    "#             mean_list[i] = (mean_list[i-1] + mean_list[i+1])/2\n",
    "    return mean_list\n",
    "\n",
    "\n",
    "def CL_percent_delta(df, apply_col, seg_col, use_predict = True, up=90, down=10, correct_idx=[]):\n",
    "    '''\n",
    "    描述：\n",
    "        统计每一段时间的某一列变化范围，并拟合曲线\n",
    "        注意：需要手动给出correct_idx\n",
    "    '''\n",
    "    data = df[apply_col]\n",
    "    seg_cl = df[seg_col].unique()\n",
    "    seg_cl.sort()\n",
    "    \n",
    "    delta_list = []\n",
    "    for cl in seg_cl:\n",
    "        tmp = data[df[seg_col] == cl]\n",
    "        up_bound = np.percentile(tmp, up)\n",
    "        down_bound = np.percentile(tmp, down)\n",
    "        cl_delta = up_bound - down_bound\n",
    "        delta_list.append(cl_delta)\n",
    "    if not use_predict:\n",
    "        return delta_list\n",
    "    # 训练gpr模型，预测性地纠正错误的点\n",
    "    ser = pd.Series(delta_list)\n",
    "    ser.index = seg_cl\n",
    "    \n",
    "    cl = np.array(correct_idx)*5\n",
    "    x_train = [idx for idx in ser.index if idx not in cl]\n",
    "    y_train = ser[x_train].values.reshape(-1,1)\n",
    "    x_train = np.array(x_train).reshape(-1,1)\n",
    "#     print(x_train, y_train)\n",
    "    rbf_kernel = GPy.kern.RBF(input_dim=1, variance=1., lengthscale=5.)\n",
    "    m = GPy.models.GPRegression(x_train, y_train, rbf_kernel)\n",
    "    m.optimize_restarts(num_restarts=50, verbose=False)\n",
    "\n",
    "    \n",
    "    preds = m.predict(np.array([cl]).reshape(-1,1))[0].ravel()\n",
    "    ser[cl] = preds\n",
    "    ser[ser<0] = ser[ser>0].min()*0.9\n",
    "    delta_list = list(ser)\n",
    "    return delta_list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def CL_mean_auto(df, apply_col, seg_col, up_thred = 30, down_thred= 0, use_pretict=True):\n",
    "    '''\n",
    "    描述：\n",
    "        统计每一段时间的某一列均值，并拟合曲线\n",
    "    '''\n",
    "    data = df[apply_col]\n",
    "    seg_idx = df[seg_col].unique()\n",
    "    seg_idx.sort()\n",
    "    \n",
    "    mean_list = []\n",
    "    # 标记超出阈值的样本为-1\n",
    "    for idx in seg_idx:\n",
    "        idx_mean = data[df[seg_col] == idx].mean()\n",
    "        mean_list.append(idx_mean)\n",
    "        if (mean_list[-1]>up_thred) or (mean_list[-1]<down_thred):\n",
    "            print('标记超出阈值的样本:',mean_list[-1], idx)\n",
    "            mean_list[-1] = -1\n",
    "    \n",
    "    # 拟合粗趋势\n",
    "    ser = pd.Series(mean_list)\n",
    "    ser.index = range(ser.shape[0]) #index\n",
    "    x_train = ser.index[ser != -1].values\n",
    "    y_train = ser[ser != -1].values\n",
    "    \n",
    "    f1 = np.polyfit(x_train, y_train, 2)\n",
    "    p1 = np.poly1d(f1)\n",
    "    tmp = p1(ser.index.values)\n",
    "    mean_list = list(tmp)\n",
    "    \n",
    "    # 纠正\n",
    "    for i,idx in enumerate(seg_idx):\n",
    "        idx_mean = data[df[seg_col] == idx].mean()\n",
    "        cmp_mean = mean_list[i]\n",
    "        if use_pretict and (np.abs(idx_mean - cmp_mean) >= 1.5):\n",
    "            pass\n",
    "        else:\n",
    "            mean_list[i] = idx_mean \n",
    "            \n",
    "    return mean_list\n",
    "\n",
    "def cl_mean(mean_list, up_thred = 30, down_thred= 0, confidence=0.5):\n",
    "    mean_list_copy = mean_list\n",
    "     # 标记超出阈值的样本为-1\n",
    "    for i in range(len(mean_list)):\n",
    "        idx_mean = mean_list[i]\n",
    "        if (idx_mean>up_thred) or (idx_mean<down_thred):\n",
    "            mean_list[i] = -1\n",
    "    \n",
    "    # 拟合粗趋势\n",
    "    ser = pd.Series(mean_list)\n",
    "    ser.index = range(ser.shape[0]) #index\n",
    "    x_train = ser.index[ser != -1].values\n",
    "    y_train = ser[ser != -1].values\n",
    "    \n",
    "    f1 = np.polyfit(x_train, y_train, 2)\n",
    "    p1 = np.poly1d(f1)\n",
    "    tmp = p1(ser.index.values)\n",
    "    mean_list = list(tmp)\n",
    "    \n",
    "    # 纠正\n",
    "    for i in ser.index:\n",
    "        idx_mean = mean_list_copy[i]\n",
    "        cmp_mean = mean_list[i]\n",
    "        if np.abs(idx_mean - cmp_mean) >= confidence:\n",
    "            pass\n",
    "        else:\n",
    "            mean_list[i] = idx_mean \n",
    "            \n",
    "    return mean_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_01 = load_data(1)\n",
    "train_02 = load_data(2)\n",
    "train_03 = load_data(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "标记超出阈值的样本: 98.04703808479266 70\n",
      "标记超出阈值的样本: 34.95250718521592 210\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f1cf82c2908>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "mean_list = CL_mean_auto(train_01, 'PCA_T2', 'CLI', 30, 0, use_pretict=True)\n",
    "mean_list = cl_mean(mean_list, confidence=0.3)\n",
    "mean_list[0] = 0\n",
    "plt.plot(range(1,49), mean_list, label='train_01')\n",
    "\n",
    "mean_list = CL_mean_auto(train_02, 'PCA_T2', 'CLI', 30, 0, use_pretict=True)\n",
    "mean_list = cl_mean(mean_list, confidence=0.3)\n",
    "mean_list[0] = 0\n",
    "plt.plot(range(1,49), mean_list, label='train_02')\n",
    "\n",
    "mean_list = CL_mean_auto(train_03, 'PCA_T2', 'CLI', 30, 0, use_pretict=True)\n",
    "mean_list = cl_mean(mean_list, confidence=0.3)\n",
    "mean_list[0] = 0\n",
    "plt.plot(np.array(range(1,38)), np.array(mean_list), label='train_03')\n",
    "# plt.plot(np.array(range(1,38)) * 48/37, np.array(mean_list) * 48/37, label='train_03')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f1cf07b9780>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 手动\n",
    "plt.figure(figsize=(10,5))\n",
    "delta_list = CL_percent_delta(df=train_01, apply_col='PCA_T2', seg_col='CLI', use_predict = True, up=90, down=10, correct_idx=[1, 13, 14, 36, 37, 48])\n",
    "ser = pd.Series(delta_list)\n",
    "ser.index = range(1,49)\n",
    "plt.plot(delta_list, label='train_01')\n",
    "\n",
    "# plt.figure(figsize=(10,5))\n",
    "delta_list = CL_percent_delta(df=train_02, apply_col='PCA_T2', seg_col='CLI', use_predict = True, up=90, down=10, correct_idx=[14, 21, 35, 38, 42, 43, 46, 48])\n",
    "ser = pd.Series(delta_list)\n",
    "ser.index = range(1,49)\n",
    "plt.plot(ser, label='train_02')\n",
    "\n",
    "\n",
    "# plt.figure(figsize=(10,5))\n",
    "delta_list = CL_percent_delta(df=train_03, apply_col='PCA_T2', seg_col='CLI', use_predict = True, up=90, down=10, correct_idx=[1, 8, 20, 30 , 33, 36, 37])\n",
    "ser = pd.Series(delta_list)\n",
    "ser.index = range(1,38)\n",
    "plt.plot(ser, label='train_03')\n",
    "\n",
    "plt.legend(loc=4)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
